Segmentation refers to a computer process that partitions a digital image into multiple sets of pixels, each set referred to as a region. The result of image segmentation is a set of regions that together cover the entire image and/or other data extracted from the image.
In general, segmentation may be used to locate objects and boundaries within an image. More particularly, image segmentation is used in object-based image analysis, which partitions remotely sensed imagery into meaningful image objects. In contrast to traditional pixel-based image analysis, an object-based approach provides additional spatial, spectral, and contextual features (such as shape, size, textural, and context relations) to facilitate more powerful image analysis.
However, segmenting a large multi-spectral image (e.g., on the order of tens to hundreds of megapixels) is a difficult task, as taking an entire large image as a single processing unit is neither practical nor feasible. This is primarily because of computational costs and memory resource constraints.
As a result, tiling and merging procedures are used as one solution to the large image segmentation problem. For example, one approach divides a large image into smaller overlapping tiles and then handles ambiguities in the overlapping areas. However, with this approach it is difficult to determine a good overlapping ratio; a large overlap reduces artifacts but significantly increases computational time. Further, the segmentation of each individual tile is not parallelized, and there is no guarantee that artificial boundaries will be removed.
Another approach generally referred to as “region assignment swapping” has been used in Recursive Hierarchical Segmentation (RHSEG), which addresses the processing window artifacts problem when segmenting a large image. The region assignment swapping approach attempts to identify pairs of regions that contain pixels that are actually more similar to other regions in the pair, and then reassigns the pixels in one region to the other region. However, this approach tends to introduce spatially non-connected regions, whereby connected component region labeling needs to be run on the resulting region labeling to restore the spatial connectivity of the regions. This increases the processing time substantially. Moreover, RHSEG is generally not directed towards segmenting large images, but rather on exploiting the image content from a segmentation hierarchy.
To classify objects in an image, such as buildings, trees, grass, and water bodies, pixel-based image classification is used. However, with current technology, the results are unsatisfactory in a number of aspects, including with respect to fuzzy edges, a general difficulty in integrating object-specific information such as shape, height and size, general inflexibility with respect to post processing, and difficulties in applying human knowledge for use in the classification.